New optimized configuration for a hybrid PV/diesel/battery system based on coyote optimization algorithm: A case study for Hotan county

The main objective of this study is to present a multi-objective and optimal hybrid PV/diesel generator/battery Renewable Energy System (HRES) to provide this reliability in the Hotan county, placed in Taklamakan Desert. This study uses the epsilon-constraint method along with a developed version of the coyote optimization algorithm to achieve the best values of the component sized to decrease the loss of load probability, CO2 emission value, and the annualized cost of the system. Sensitivity analysis also is performed to show each component's impact on the system. The results demonstrate that the DG backup system improves the yearly cost of the system from 8347.2 $ to 9318.4 $, which shows about 10.42% increasing by increasing the fuel consumption. Here, the LLP increases from 0% to 9.19% and the CO2 emissions improve from 2531.2 kg/yr to 13257 kg/yr. Accordingly, the COE value is reduced from 0.39 $/kWh to 0.24 $/kWh over the PV penetration, reducing from 92.27% to 59.42%. This decreasing indicates that the system fuel cost has more impact than the cost of PV on the COE, which is due to the low cost required of conventional power production than the PV system. The results also indicate a noteworthy upshot on the battery storage unit size such that the size of epsilon(CO2) has been enhanced from 27.4 kWh to 50 kWh in the range from 7000 kg/year to 25 kg/year. The results also are compared with the PSO-based optimal system and HOMER software results to show its excellence toward them. (c) 2022 The Author(s). Published by Elsevier Ltd.

Automated summary

The main objective of this study is to propose a multi-objective and optimal hybrid PV/diesel generator/battery Renewable Energy System (HRES) to ensure reliability in Hotan county, located in the Taklamakan Desert. The study adopts the epsilon-constraint method and an improved coyote optimization algorithm to determine the optimal component sizes, considering factors such as loss of load probability, CO2 emissions, and system cost. Sensitivity analysis is also conducted to assess the impact of each component. The results show that implementing a DG backup system increases the annual cost of the system by 10.42% due to higher fuel consumption. Furthermore, the LLP increases from 0% to 9.19% and CO2 emissions increase from 2531.2 kg/yr to 13257 kg/yr. The cost of electricity (COE) decreases from 0.39 $/kWh to 0.24 $/kWh as PV penetration decreases from 92.27% to 59.42%. These findings indicate that the fuel cost of the system has a greater influence on the COE than the cost of PV, primarily due to the lower cost of conventional power production. Additionally, the study reveals the significant impact of battery storage unit size on CO2 emissions. The size of epsilon(CO2) increases from 27.4 kWh to 50 kWh within a range of 7000 kg/year to 25 kg/year. The results are compared with those obtained using a PSO-based optimal system and HOMER software, demonstrating the superiority of the proposed approach.